Coronary artery disease (CAD) is a leading cause of death in the developed world. The reference diagnostic imaging exam for the diagnosis of CAD is coronary angiography that enables the detection of blockages or obstructions in the coronary arteries through the buildup of plaque. Coronary angiography is an invasive exam that would be prohibitive to apply to a large asymptomatic population for the purpose of earlier detection of the disease.
Coronary artery calcification (CAC) or coronary artery calcium scoring (CACS) is a good indicator of the presence of plaque and can be imaged using non-invasive methods like computed tomography (CT). Cardiac CT is particularly useful in assisting medical providers in assessing the risk of cardiovascular disease that can lead to heart failure or a stroke. One particular implementation of cardiac CT is the detection of calcium deposits in the coronary arteries of medical patients. These deposits have to be reviewed by a physician and labeled according to the artery location and a total score is reported using the most common algorithm. Such manual procedures are labor intensive and time consuming, as well as being prone to error in exact positioning. Further, many objects in the image, such as calcified plaque, have an irregular margin such that a fixed geometry will be over inclusive by containing non-calcified tissue or will be under inclusive by omitting a portion of the calcified plaque.
For the reasons stated above, and for other reasons stated below which will become apparent to those skilled in the art upon reading and understanding the present specification, there is a need in the art for accurate detection and labeling of coronary artery calcification using an automated method. There is also a need for improved segmentation technique, three dimensional (3D) image processing, and region labels that in combination generate calcium score automatically.